Operations | Monitoring | ITSM | DevOps | Cloud

Revolutionizing User Experience with Agentic AI

Revolutionizing User Experience with Agentic AI Agentic AI integration creates prompt-driven interfaces that simplify access to information and ticketing for users. AI incident correlation reduces service downtime, boosting productivity. Ring deployment automates patch management for controlled updates, while lifecycle management of gateways enhances security. Autonomous endpoint management tackles time and data challenges, leading to more efficient operations. The focus is on leveraging technology to innovate and optimize resources.

AI + Dark Mode: Introducing AI-Powered Insights and The Long Awaited Dark Mode

Join the live stream at 11 am ET, here. Launch Week’s Friday drop delivers two of the most-requested upgrades we’ve ever shipped: Together, they turn Bindplane into a cooler , and smarter , place to manage observability and SecOps telemetry. A full suite of extensive AI features will be rolling out over the coming weeks. This is just the beginning!

Why Cribl Copilot Editor is Built for the Human, First and Foremost

I’m genuinely excited about what we're rolling out with Copilot Editor, an update to our AI that’s truly packed with new capabilities designed to help you automate pipeline development. You can read about these capabilities here. I wanted to take a moment to share our thinking on a core principle that guides how we build, especially regarding the impactful, and sometimes daunting, world of generative AI.

From RPA to Agentic AI: Understanding the Shifting Landscape of Enterprise Automation

Over the past decade, organizations have embraced automation in waves – starting with basic task scripts and Robotic Process Automation (RPA), then moving to hyperautomation, and now exploring “agentic AI” as the next frontier. Each step in this evolution has expanded the scope of what can be automated, and revealed new challenges. This blog offers a detailed comparison of RPA, hyperautomation, and agentic AI, their key differences, strategic advantages, and potential drawbacks.

Hyperparameter tuning for LLMs using CircleCI matrix workflows

Hyperparameter tuning is a critical step in optimizing large language models (LLMs). Parameters such as learning rate, batch size, weight decay, and number of training epochs can significantly affect convergence behavior and final model performance. While several approaches like grid search or random search are widely used, executing them manually is inefficient; especially when each training run is compute-intensive.

AI in Action with Kunal Kushwaha: 2 Demo Showcase. See What's Possible!

Join Kunal Kushwaha, Field CTO at Civo, for two demos using relaxAI. In the first demo, we'll show you how to deploy your own Large Language Model (LLM) inference engine using Ollama, giving you full control over your AI model. In the second demo, we'll demonstrate how to build custom AI integrations using relaxAI API, making it easy to add AI features to your existing applications. Whether you're an AI developer, MLOps team, or just curious about AI, this video is for you.